Our work crucially relies on a disentangled representation to induce a space where the similarity of individuals is based on attributes that are not correlated to the sensitive attribute. In this space, [1]} investigate the usefulness of unsupervised disentanglement approaches for making fair predictions. They show that both the optimal and empirical predictions may be unfair. Since the sensitive attribute is not known at training, an assumption is made that the sensitive attribute and the label are independent, which is not true in many real-world situations. Our approach does not make such an assumption and instead builds on the disentanglement approach of [2]}, which uses the certain attribute \(a\)  to create a representation that is independent of \(a\) . The authors of [3]} propose a disentanglement approach which does not make use of labels \(y\)  at training. Similarly to our method, two representations are learned from \(x\) , one relevant to \(a\) , and one irrelevant to it.
Compared to [3]},
our method directly optimizes the fair representation with respect to the sensitive attribute and target classification task and thus allows the control of the tradeoff between fairness and accuracy.
